Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations | Paper Citation Record

Observation 265a53f4-4bb0-4ab5-90c7-dbc42e447527 · outbound

This paper cites Imagenet classi- ﬁcation with deep convolutional neural networks,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Imagenet classi- ﬁcation with deep convolutional neural networks,

Reference 1

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This paper cites Very deep convolutional net- works for large-scale image recognition,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Very deep convolutional net- works for large-scale image recognition,

Reference 2

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This paper cites Deep residual learning for image recognition,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Deep residual learning for image recognition,

Reference 3

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Observation 5946c2d6-16e6-49d0-a7fa-e5b0f3e0e143 · outbound

This paper cites Discrimination-aware channel pruning for deep neural networks,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Discrimination-aware channel pruning for deep neural networks,

Reference 4

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Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Channel pruning for accelerating very deep neural networks,

Reference 5

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This paper cites Pruning ﬁlters for efﬁcient convnets,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Pruning ﬁlters for efﬁcient convnets,

Reference 6

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This paper cites Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications

Reference 7

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This paper cites Accelerating very deep convolutional networks for classiﬁcation and detection,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Accelerating very deep convolutional networks for classiﬁcation and detection,

Reference 8

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Observation 5dc60e0a-b716-49ac-84eb-1043c0f6ec1d · outbound

This paper cites Incremental network quantization: Towards lossless cnns with low-precision weights,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Incremental network quantization: Towards lossless cnns with low-precision weights,

Reference 9

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This paper cites Binaryconnect: Train- ing deep neural networks with binary weights during propaga- tions,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Binaryconnect: Train- ing deep neural networks with binary weights during propaga- tions,

Reference 10

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This paper cites Trained ternary quanti- zation,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Trained ternary quanti- zation,

Reference 11

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Observation ee421ceb-6eaf-4bdb-b223-5940f0d0dde2 · outbound

This paper cites DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

Reference 12

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This paper cites Single Path One-Shot Neural Architecture Search with Uniform Sampling.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Single Path One-Shot Neural Architecture Search with Uniform Sampling

Reference 13

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Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 14

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Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Shufﬂenet: An extremely efﬁcient convolutional neural network for mobile devices,

Reference 15

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This paper cites Dropout: a simple way to prevent neural networks from overﬁtting,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Dropout: a simple way to prevent neural networks from overﬁtting,

Reference 16

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This paper cites Deep networks with stochastic depth,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Deep networks with stochastic depth,

Reference 17

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This paper cites Fitnets: Hints for thin deep nets,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Fitnets: Hints for thin deep nets,

Reference 18

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Observation b991f2bf-b278-48dd-9b65-ab7f7a19c3e1 · outbound

This paper cites Distilling the knowledge in a neural network,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Distilling the knowledge in a neural network,

Reference 19

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Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Actor-mimic: Deep multitask and transfer reinforcement learning,

Reference 20

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This paper cites Paying more attention to atten- tion: Improving the performance of convolutional neural networks via attention transfer,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Paying more attention to atten- tion: Improving the performance of convolutional neural networks via attention transfer,

Reference 21

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This paper cites Do deep nets really need to be deep?.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Do deep nets really need to be deep?

Reference 22

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Observation 23b0ee56-604f-4907-a6cd-4d965fdd1321 · outbound

This paper cites Towards effective low-bitwidth convolutional neural networks,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Towards effective low-bitwidth convolutional neural networks,

Reference 23

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This paper cites Xnor- net: Imagenet classiﬁcation using binary convolutional neural networks,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Xnor- net: Imagenet classiﬁcation using binary convolutional neural networks,

Reference 24

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This paper cites Binarized neural networks,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Binarized neural networks,

Reference 25

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This paper cites Training Competitive Binary Neural Networks from Scratch.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Training Competitive Binary Neural Networks from Scratch

Reference 26

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This paper cites Learning to Train a Binary Neural Network.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Learning to Train a Binary Neural Network

Reference 27

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Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations How to train a compact binary neural network with high accuracy?

Reference 28

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Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Network sketching: Exploiting binary structure in deep cnns,

Reference 29

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Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Bi- real net: Enhancing the performance of 1-bit cnns with improved representational capability and advanced training algorithm,

Reference 30

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Observation f00ab11b-d0b2-4d58-9571-b7f68f2cb6c2 · outbound

This paper cites PACT: Parameterized Clipping Activation for Quantized Neural Networks.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations PACT: Parameterized Clipping Activation for Quantized Neural Networks

Reference 31

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Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Learned step size quantization,

Reference 32

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Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Strutured binary neural network for accurate image classiﬁcation and semantic segmentation,

Reference 33

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Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Towards accurate binary convolu- tional neural network,

Reference 34

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Observation 9cf2b8d1-447d-47b1-8efe-c07a3a8f1ac6 · outbound

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Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Deep learning with low precision by half-wave gaussian quantization,

Reference 35

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Observation cc54c0e2-0966-4dfc-b7fd-d09bbd51b74a · outbound

This paper cites Lq-nets: Learned quanti- zation for highly accurate and compact deep neural networks,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Lq-nets: Learned quanti- zation for highly accurate and compact deep neural networks,

Reference 36

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Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Learning to quantize deep networks by optimizing quantization intervals with task loss,

Reference 37

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Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 38

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Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Loss-aware weight quantization of deep networks,

Reference 39

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Observation 52f00158-dd4d-4b91-a41e-1b6a17b31d8a · outbound

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Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Regularizing activation distribution for training binarized deep networks,

Reference 40

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Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Learning Sparse Low-Precision Neural Networks With Learnable Regularization

Reference 41

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Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Proxquant: Quantized neural networks via proximal operators,

Reference 42

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Observation 53c5c8ab-049e-4617-a20d-624a455f8b27 · outbound

This paper cites Weighted-entropy-based quantization for deep neural networks,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Weighted-entropy-based quantization for deep neural networks,

Reference 43

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Observation 7524f36f-5e57-4aa7-96c4-805e1a12d3d0 · outbound

This paper cites Model compression via distillation and quantization,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Model compression via distillation and quantization,

Reference 44

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Observation fe38bff5-31e7-4385-8fb8-9b467ffab927 · outbound

This paper cites Relaxed quantization for discretized neural networks,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Relaxed quantization for discretized neural networks,

Reference 45

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Observation 3fcb5f41-774b-4f0c-88e8-f292041c6dfd · outbound

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Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Ai benchmark: Running deep neural networks on android smartphones,

Reference 46

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Observation 34429bc4-0dd8-4b50-b7a4-36f2f0e932f5 · outbound

This paper cites Bmxnet: An open- source binary neural network implementation based on mxnet,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Bmxnet: An open- source binary neural network implementation based on mxnet,

Reference 47

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Observation 86d7dc27-349e-4a47-a2b0-0f27c91faba1 · outbound

This paper cites Finn: A framework for fast, scalable binarized neural network inference,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Finn: A framework for fast, scalable binarized neural network inference,

Reference 48

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Observation 218431b1-259b-4b4f-ada2-a0ed8d782ff5 · outbound

This paper cites Quantization and training of neural networks for efﬁcient integer-arithmetic-only inference,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Quantization and training of neural networks for efﬁcient integer-arithmetic-only inference,

Reference 49

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Observation bcca7abf-3ead-49e6-9812-850d09999b46 · outbound

This paper cites SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

Reference 50

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Observation 7913ab61-92fc-4869-968f-e29e57559e90 · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Xception: Deep learning with depthwise separable convolutions,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.903543Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0c117e3a-0946-4c07-8608-f948f99a5c0d · outbound

This paper cites Neural architecture search with reinforce- ment learning,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Neural architecture search with reinforce- ment learning,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.892727Z

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Observation eada9d2d-2f69-4843-8a38-38706de56089 · outbound

This paper cites Efﬁcient neural architecture search via parameter sharing,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Efﬁcient neural architecture search via parameter sharing,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.881872Z

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Observation c1fd34d8-4d32-4a24-9493-71dabf8b0786 · outbound

This paper cites Learning trans- ferable architectures for scalable image recognition,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Learning trans- ferable architectures for scalable image recognition,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.871738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4a78391b-ce66-42ed-9917-ced52255abff · outbound

This paper cites Progressive neural architecture search,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Progressive neural architecture search,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.860742Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a8899592-514d-452d-9cd2-25e912e36c5a · outbound

This paper cites Regularized evolution for image classiﬁer architecture search,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Regularized evolution for image classiﬁer architecture search,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.849922Z

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Observation dfec432b-075a-4ba9-9b92-4a78ed01d361 · outbound

This paper cites Darts: Differentiable architec- ture search,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Darts: Differentiable architec- ture search,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.839512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 34e68636-a082-4afc-ad73-1573cf2cbeb0 · outbound

This paper cites Proxylessnas: Direct neural architec- ture search on target task and hardware,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Proxylessnas: Direct neural architec- ture search on target task and hardware,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.828912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9a13f3c4-36be-440f-ba6d-bb5ae4b042b7 · outbound

This paper cites Nisp: Pruning networks using neuron importance score propagation,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Nisp: Pruning networks using neuron importance score propagation,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.818125Z

Source-reported events for the cited work

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Observation 001a5c2e-020f-4856-b949-c2dc9f1b575c · outbound

This paper cites N2n learning: Network to network compression via policy gradient reinforcement learning,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations N2n learning: Network to network compression via policy gradient reinforcement learning,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.808126Z

Source-reported events for the cited work

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Observation 46f7e222-208b-4f6d-8514-679a5208de94 · outbound

This paper cites Amc: Automl for model compression and acceleration on mobile devices,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Amc: Automl for model compression and acceleration on mobile devices,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.796467Z

Source-reported events for the cited work

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Observation 74859d55-4c8d-4dab-8943-0b6a9e022d4e · outbound

This paper cites Clip-q: Deep network compression learning by in-parallel pruning-quantization,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Clip-q: Deep network compression learning by in-parallel pruning-quantization,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.785179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 99f8b110-f748-45b9-8fbe-101c4c94ef8d · outbound

This paper cites Network pruning via transformable archi- tecture search,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Network pruning via transformable archi- tecture search,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.774199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c0bbc9cc-d338-4577-8ba6-2cb73d7c5c07 · outbound

This paper cites Real-time action recognition with enhanced motion vector cnns,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Real-time action recognition with enhanced motion vector cnns,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.762414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 72c2aee5-4015-4cf2-b598-8a9a285be0d1 · outbound

This paper cites Learning efﬁcient object detection models with knowledge distillation,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Learning efﬁcient object detection models with knowledge distillation,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.750837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c3f4967b-cdb9-4302-b29e-605e2de43d58 · outbound

This paper cites Quantization mimic: Towards very tiny cnn for object detection,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Quantization mimic: Towards very tiny cnn for object detection,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.738508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b72199ad-b861-4f59-8180-876cc641cc33 · outbound

This paper cites Knowledge Adaptation for Efficient Semantic Segmentation.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Knowledge Adaptation for Efficient Semantic Segmentation

Reference 67

Resolution: verified exact; local_arxiv, observed 2026-08-14T14:07:19.476310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9a0d1b12-bc4e-418c-8931-068ec31e5d27 · outbound

This paper cites Apprentice: Using knowledge distillation techniques to improve low-precision network accuracy,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Apprentice: Using knowledge distillation techniques to improve low-precision network accuracy,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.726897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation adbdbb27-adc4-4400-bae7-4bfd8245a52e · outbound

This paper cites Maxout networks,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Maxout networks,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.714795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f1969898-ec83-42de-9364-74aa34be39b4 · outbound

This paper cites Regular- ization of neural networks using dropconnect,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Regular- ization of neural networks using dropconnect,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.703415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation db85ff43-4537-4e11-bfcc-41e2ce25ff67 · outbound

This paper cites Gradual dropin of layers to train very deep neural networks,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Gradual dropin of layers to train very deep neural networks,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.692151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e8601791-bb3a-4a8b-b432-2bb22c5569ad · outbound

This paper cites Learning accurate low-bit deep neural networks with stochastic quantization,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Learning accurate low-bit deep neural networks with stochastic quantization,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.679328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 71dcdcc4-8310-4a6d-b5d6-19659a9a6c59 · outbound

This paper cites Slimmable neural networks,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Slimmable neural networks,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.666789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6de34da9-41cd-4ca7-bafc-bd4bdff8295b · outbound

This paper cites Universally slimmable networks and improved training techniques,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Universally slimmable networks and improved training techniques,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.655777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f9a3dd58-45a7-4d79-b934-2d46fd4a79f1 · outbound

This paper cites Learning multiple layers of fea- tures from tiny images,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Learning multiple layers of fea- tures from tiny images,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.644900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a0532080-bb6c-4cd1-95c5-976d78591af3 · outbound

This paper cites Imagenet large scale visual recognition challenge,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Imagenet large scale visual recognition challenge,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.632331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b882b96a-62d2-43da-ae51-36420690b438 · outbound

This paper cites Identity mappings in deep residual networks,.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Identity mappings in deep residual networks,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:07:19.620154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 442bf252-e88f-4671-884a-d94fc091b194 · outbound

This paper cites Precision Highway for Ultra Low-Precision Quantization.

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations Precision Highway for Ultra Low-Precision Quantization

Reference 78

Resolution: verified exact; local_arxiv, observed 2026-08-14T14:07:19.459821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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